A flexible intelligent control system for microgrid loads

Abstract

The present invention relates to the field of smart grid technology, and discloses a microgrid load flexible intelligent management and control system, including: a distributed energy module, an operating equipment system module, a microgrid measurement and control module, an energy decision module, an energy drive module and an operation drive module. By implementing the present invention, non-renewable energy can be accurately driven to generate electricity during peak power consumption, and renewable energy can be relied on to generate electricity during valley power consumption, so as to achieve accurate and stable power supply. At the same time, the excess electric energy can be stored and used to power the microgrid measurement and control module, thereby improving energy utilization efficiency. It solves the problem of intermittent supply and demand of energy in industrial fields such as oil fields due to the large energy demand and strong intermittency of operating equipment under the condition of load flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a microgrid load flexible intelligent management and control system. Background Art

[0002] As the country pays more and more attention to energy conservation and emission reduction, my country's energy structure is undergoing profound changes. At present, my country's power supply structure is characterized by oversupply. In order to effectively save energy consumption, the load-follow-source dispatching mode has gradually attracted attention. By configuring energy storage equipment, such as battery energy storage systems and pumped storage power stations, electric energy can be released during peak power supply periods and stored during low power supply periods. At the same time, multi-energy complementary technologies, such as solar energy and wind energy complementation, wind energy and hydropower complementation, etc., can be used to improve the stability and reliability of energy supply.

[0003] However, in industrial fields such as oil fields, due to the large energy demand and strong intermittent nature of operating equipment, how to solve the problem of intermittent energy supply and demand under the condition of load flexibility has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of this, the present invention provides a microgrid load flexible intelligent management and control system to solve the problem of intermittent energy supply and demand in industrial fields such as oil fields due to large energy demand and strong intermittency of operating equipment under load flexibility.

[0005] The present invention provides a microgrid load flexible intelligent management and control system, the system comprising: a distributed energy module, including a power generation submodule and an energy storage submodule, the power generation submodule is used to supply energy to the operating equipment system module of the target oil field, the energy storage submodule is used to power the microgrid measurement and control module, wherein the power generation submodule comprises a first distributed power generation unit and a second distributed power generation unit, the power generation mode of the first distributed power generation unit is renewable energy power generation, and the second distributed power generation unit is non-renewable energy power generation; the operating equipment system module comprises a machine mining unit, a heat unit, a water injection unit and an auxiliary unit, which are used to perform oil and gas mining operations on the target oil field; the microgrid measurement and control module is used to collect energy from the first distributed power generation unit Equipment data, environmental data of the target oil field and operating equipment data of each operating equipment in the operating equipment system module; an energy decision module, which is used to input energy equipment data, operating equipment data, environmental data, historical energy equipment data, historical operating equipment data, historical environmental data and target oil field operating plan data into the power generation strategy model to obtain power generation strategy data, and the above historical energy equipment data, historical operating equipment data and target oil field operating plan data are stored in a database; an energy drive module, which is used to drive the second distributed power generation unit and the energy storage submodule based on the above power generation strategy data; an operating drive module, which is used to drive the operating equipment in the above operating equipment system module in sequence based on the operating plan data of the target oil field.

[0006] The present embodiment provides a microgrid load flexible intelligent control system. First, through the distributed energy module, a stable energy supply to the target oilfield operating equipment system module is realized. When the renewable energy supply is insufficient, the non-renewable energy is used to supplement the power supply, ensuring the power supply stability of the microgrid measurement and control module. Through the operating equipment system module, the oil and gas extraction operation of the target oilfield is realized, ensuring the normal production of the oilfield. Secondly, through the microgrid measurement and control module, the energy equipment data of the first distributed power generation unit, the environmental data of the target oilfield and the operating data of each operating equipment in the operating equipment system module can be collected in real time, providing an accurate information basis for energy decision-making. Through the energy decision-making module, multiple data can be input into the power generation strategy model to obtain optimized power generation strategy data, improve the efficiency and stability of energy utilization, and take into account historical data and operation plan data, making the power generation strategy more scientific and reasonable. Through the energy drive module, the second distributed power generation unit and the energy storage submodule can be accurately driven based on the power generation strategy data, realizing the on-demand allocation and efficient utilization of energy. Through the operation drive module, the operation equipment in the operation equipment system module can be driven in order based on the operation plan data of the target oil field, ensuring the orderly progress of oil field operations and improving operation efficiency and safety. By implementing the present invention, non-renewable energy can be accurately driven to generate electricity during peak power consumption, and renewable energy can be relied on to generate electricity during valley power consumption, achieving accurate and stable power supply. At the same time, the excess electric energy can be stored for powering the microgrid measurement and control module, thereby improving energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0008] Figure 1 It is a structural schematic diagram of a microgrid load flexible intelligent management and control system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0010] As the country pays more and more attention to energy conservation and emission reduction, my country's energy structure is undergoing profound changes. At present, my country's power supply structure is characterized by oversupply. In order to effectively save energy consumption, the load-follow-source dispatching mode has gradually attracted attention. By configuring energy storage equipment, such as battery energy storage systems and pumped storage power stations, electric energy can be released during peak power supply periods and stored during low power supply periods. At the same time, multi-energy complementary technologies, such as solar energy and wind energy complementation, wind energy and hydropower complementation, etc., can be used to improve the stability and reliability of energy supply.

[0011] However, in industrial fields such as oil fields, due to the large energy demand and strong intermittent nature of operating equipment, how to solve the problem of intermittent energy supply and demand under the condition of load flexibility has become a technical problem that needs to be solved urgently.

[0012] The present embodiment provides a microgrid load flexible intelligent control system. First, through the distributed energy module, a stable energy supply to the target oilfield operating equipment system module is realized. When the renewable energy supply is insufficient, the non-renewable energy is used to supplement the power supply, ensuring the power supply stability of the microgrid measurement and control module. Through the operating equipment system module, the oil and gas extraction operation of the target oilfield is realized, ensuring the normal production of the oilfield. Secondly, through the microgrid measurement and control module, the energy equipment data of the first distributed power generation unit, the environmental data of the target oilfield and the operating data of each operating equipment in the operating equipment system module can be collected in real time, providing an accurate information basis for energy decision-making. Through the energy decision-making module, multiple data can be input into the power generation strategy model to obtain optimized power generation strategy data, improve the efficiency and stability of energy utilization, and take into account historical data and operation plan data, making the power generation strategy more scientific and reasonable. Through the energy drive module, the second distributed power generation unit and the energy storage submodule can be accurately driven based on the power generation strategy data, realizing the on-demand allocation and efficient utilization of energy. Through the operation drive module, the operation equipment in the operation equipment system module can be driven in order based on the operation plan data of the target oil field, ensuring the orderly progress of oil field operations and improving operation efficiency and safety. By implementing the present invention, non-renewable energy can be accurately driven to generate electricity during peak power consumption, and renewable energy can be relied on to generate electricity during valley power consumption, achieving accurate and stable power supply. At the same time, the excess electric energy can be stored for powering the microgrid measurement and control module, thereby improving energy utilization efficiency.

[0013] In view of the technical problems mentioned in the background technology, the present application embodiment provides a microgrid load flexible intelligent management and control system, see Figure 1 As shown, the system includes: a distributed energy module, an operating equipment system module, a microgrid measurement and control module, an energy decision module, an energy drive module and an operating drive module. The functions of each of the above components are introduced below.

[0014] The distributed energy module includes a power generation submodule and an energy storage submodule. The power generation submodule is used to supply energy to the operating equipment system module of the target oil field, and the energy storage submodule is used to supply power to the microgrid measurement and control module. The power generation submodule includes a first distributed power generation unit and a second distributed power generation unit. The first distributed power generation unit generates electricity using renewable energy, and the second distributed power generation unit generates electricity using non-renewable energy.

[0015] Specifically, the first distributed generation unit uses renewable energy generation, including solar energy, wind energy, waste heat, etc., which reduces environmental pollution and improves the sustainability and utilization of energy. The second distributed generation unit generates electricity from non-renewable energy as a supplement to energy supply, ensuring that the operating equipment system module can still obtain sufficient energy support when renewable energy is insufficient or unstable. At the same time, the energy storage submodule in the distributed energy module provides a continuous power supply for the microgrid measurement and control module, ensuring that the measurement and control module can collect and analyze various data in real time and accurately, providing strong support for energy decision-making. This design not only improves the reliability and stability of the system, but also optimizes the distribution and utilization of energy, making the entire oilfield load flexible intelligent management and control system more efficient and environmentally friendly.

[0016] The operating equipment system module includes a mechanical extraction unit, a thermal unit, a water injection unit and an auxiliary unit, which are used to carry out oil and gas extraction operations in the target oil field.

[0017] Specifically, during the operation preparation and initial production phase, the mechanical extraction unit begins to enter the operation preparation phase. At this time, equipment such as the pumping unit will be preheated and debugged to ensure that the equipment can operate normally. As the operation unfolds, the pumping unit will gradually enter a stable production state, and the power consumption will gradually increase. At this stage, due to the relatively low production pressure of the oil well, the load rate of the pumping unit may be low, but a stable power supply is still required. The thermal unit starts steam throughput or steam drive operation to heat the crude oil in the oil layer. At this stage, the power consumption of the thermal unit will be relatively high, and a large amount of steam needs to be heated and the temperature and pressure of the steam need to be maintained. The water injection unit prepares for the water injection operation, checks the status of the water injection well, adjusts the water injection pressure, etc. At this time, the power consumption of the water injection pump is low, but it will maintain a certain operating state to ensure that the water injection operation can be carried out at any time. Auxiliary units, such as generators, air compressors, and water treatment equipment, may maintain a low-load operating state to provide necessary support and guarantee for other units. In the stable production phase, the mechanical extraction unit enters a stable production phase. At this time, the pumping unit continues to operate and the power consumption will remain at a high level. As the production pressure of the oil well gradually increases, the load rate of the pumping unit will also increase accordingly. At this stage, it is necessary to pay close attention to the operating status and power consumption of the pumping unit to ensure the normal operation of the equipment and energy saving and consumption reduction. The thermal unit continues to maintain steam throughput or steam drive operation at this stage. As the crude oil is heated and the fluidity increases, the power consumption of the thermal unit may decrease slightly, but a stable power supply is still required. The water injection unit performs continuous water injection operations. At this time, the power consumption of the water injection pump will remain at a high level to ensure the stability of the formation pressure and the increase of crude oil recovery rate. The auxiliary unit maintains a stable operating state and provides necessary power, gas and water sources for other units. In the operation closing and preparation stage, the machine extraction unit gradually enters the operation closing stage. At this time, the pumping unit will gradually reduce the production volume and the power consumption will also decrease accordingly. At this stage, it is necessary to pay close attention to the operating status and power consumption of the pumping unit to ensure the normal shutdown and maintenance of the equipment. The thermal unit gradually stops steam throughput or steam drive operation at this stage to allow the oil layer to gradually cool and stabilize. At this time, the power consumption of the thermal unit will drop significantly. The water injection unit gradually stops water injection operations and performs equipment maintenance. At this time, the power consumption of the water injection pump will drop significantly. The auxiliary unit gradually reduces its load or shuts down to save electricity and reduce operating costs. The operating status and power consumption of different operating equipment in each oil well will be different at different times of the day. It is necessary to pay close attention to the operating status and power consumption of each unit to ensure the smooth progress of oil field production and the realization of energy conservation and consumption reduction.

[0018] The microgrid measurement and control module is used to collect energy equipment data of the first distributed power generation unit, environmental data of the target oil field, and operating equipment data of each operating equipment in the operating equipment system module.

[0019] Specifically, the microgrid measurement and control module is an important part of the microgrid intelligent monitoring system, responsible for collecting, processing, transmitting and storing various data related to the operation of the microgrid, using the sensor network to collect the energy equipment data of the first distributed power generation unit, such as generator voltage, current, power, etc., the environmental data of the target oil field, and the operating data of each operating equipment in the operating equipment system module. The data from the sensor network is preliminarily processed and verified to ensure the accuracy and integrity of the data.

[0020] The energy decision module is used to input energy equipment data, operating equipment data, environmental data, historical energy equipment data, historical operating equipment data, historical environmental data and target oil field operation plan data into the power generation strategy model to obtain power generation strategy data. The above historical energy equipment data, historical operating equipment data and target oil field operation plan data are stored in the database.

[0021] Specifically, the power generation strategy model includes an objective function and a neural network model, wherein the objective function is:

[0022] f(C gen_2 ,E stored ,E wasted ,L demand )=αC gen_2 +βE stored +γE wasted +δ(L demand -L supplied ) 2

[0023] Among them, C gen_2 Characterizes the power generation of the second distributed generation unit, E stored Represents the electrical energy stored in the energy storage submodule, E wasted Represents the electrical energy wasted due to excess power generation, L demand Characterizes the total load demand of the operating equipment system module, L suppliedCharacterizes the actual electric energy supplied to the operating equipment system module, α, β, γ, and δ represent the non-renewable energy weight coefficient, storage weight coefficient, waste weight coefficient, and equipment supply and demand difference weight coefficient, respectively. By introducing the power generation cost as part of the objective function, the economically feasible energy allocation scheme is emphasized. In practical applications, different non-renewable energy generation methods have different costs. Optimizing this function can help select the most cost-effective power generation method. Although the introduction of the storage system can balance supply and demand, it will also bring additional costs. By incorporating it into the objective function, the best balance between storage and power generation can be found to achieve economic optimization. Wasted energy not only means economic losses, but also may have a negative impact on the environment. By punishing the behavior of wasting energy, it encourages to improve energy efficiency and reduce unnecessary energy consumption. Mismatch between supply and demand may lead to energy shortages or surpluses, which will have a negative impact on system performance. By introducing a square term to punish the mismatch between supply and demand, the system is encouraged to match supply and demand as accurately as possible, thereby improving the stability and reliability of the system. By comprehensively considering multiple aspects such as power generation cost, storage cost, wasted energy, and supply and demand matching, the function helps to achieve comprehensive optimization of the energy system. This optimization not only helps to improve economic benefits, but also helps to reduce environmental pollution and promote sustainable development.

[0024] Furthermore, in the above power generation strategy model, the first constraint is:

[0025] P total (t) = P gen_1 (t)+P gen_2 (t)>L demand (t)

[0026] E stored (t+1)=E stored (t)+P excess (t)Δt

[0027] If P gen_1 (t)>L demand (t) and the energy storage submodule reaches the upper limit of energy storage, then E wasted (t) = P excess (t)Δt

[0028] Among them, P total (t) represents the output of the electronic module at time t, P gen_1 (t) represents the output of the first distributed generation unit at time t, P gen_2 (t) represents the output of the second distributed generation unit at time t, Δt represents the time step, L demand (t) represents the demand of the operating equipment system module at time t, and Pexcess(t) represents the additional output of the electronic module at time t. The output of the first distributed generation unit Pgen_1 is uncontrollable, its output P gen_1 (t) is known or predictable at each time step t. At each time step t, the total output must meet the needs of the operating equipment system module while taking into account the charging of the energy storage submodule. The excess power Pexcess(t) should be stored in the energy storage submodule. If the energy storage submodule is full and there is excess power, the energy will be wasted.

[0029] When the current stored power of the energy storage submodule is less than the preset threshold, the second distributed power generation unit generates power for the energy storage submodule. Using the energy storage submodule to power the microgrid measurement and control module can improve energy utilization efficiency. If the energy storage of the energy storage submodule only comes from the surplus of the first distributed power generation unit, the power supply of the microgrid measurement and control module will be unstable. Therefore, a preset threshold is set, and the second distributed power generation unit is driven to generate power for the energy storage submodule, so that the current stored power of the energy storage submodule is always higher than the preset value, so as to achieve stable power supply.

[0030] Furthermore, in the above power generation strategy model, the second constraint is:

[0031] E stored_min ≤E stored ≤E stored_max

[0032] P charge_max ≥P excess (t)≥-P discharge_max

[0033] Among them, P charge_max and P discharge_max Respectively characterize the maximum charging power and maximum discharging power of the energy storage submodule. The energy storage submodule has limited energy storage capacity and limited charging and discharging rates, so constraints are required to avoid equipment failures due to energy problems. By precisely controlling the charging and discharging process of the energy storage submodule, damage to the energy storage device caused by overcharging or discharging can be avoided, thereby extending its service life.

[0034] Furthermore, in the above power generation strategy model, the third constraint is:

[0035] L demand (t) = L mining (t)+L heat (t)+L water (t)+L aux (t)

[0036]

[0037] Among them, L mining (t), L heat (t), Lwater (t) and L aux (t) represent the load requirements of the mining unit, heat unit, water injection unit and auxiliary unit at time t, respectively. Characterizes the time series data corresponding to the job plan data, ΔL tol Characterize the load demand deviation range, It is the maximum energy supply at time t. The total load demand should be equal to the sum of the load demands of each unit, and the total load demand should be obtained based on the operation plan data of the target oil field. The operation plan data includes mining plan, production plan, maintenance plan, etc., which determines the load demand of each unit at different time points, represents the operation plan data as a series of time series data, and adds constraints to ensure that the difference between the actual load demand and the planned load demand is within an acceptable range.

[0038] The neural network model is used to determine the second solution set of the objective function based on the first solution set of the objective function, historical energy equipment data, historical operating equipment data, historical environmental data and target oilfield operation plan data. The above first solution set is obtained based on the objective function, the first constraint condition, the second constraint condition and the third constraint condition.

[0039] Specifically, the neural network model can integrate and process data from different sources, including historical energy equipment data, historical operating equipment data, historical environmental data, and target oilfield operation plan data. Through the design of its hidden layer, it can automatically extract the deep features of the input data, including linear or nonlinear combinations in the original data, reflecting the inherent laws and patterns of the data, so that the model can have a more comprehensive understanding of the actual situation of oilfield operations. The neural network model can generate more realistic solutions by learning and simulating the inherent laws and patterns of the data. Although the neural network model does not directly process the constraints during the training process, it indirectly considers their influence by learning the historical data containing these constraints. When generating the second solution set, the model will automatically adjust its output to meet these constraints based on the learned patterns and laws. Through iterative training and optimization, the neural network model can generate multiple possible solutions and select multiple second solution sets as the objective function. These solutions not only meet the requirements of the objective function, but also reflect the actual situation and constraints of oilfield operations as much as possible.

[0040] Furthermore, the power generation strategy model is also used to substitute the second solution set of the objective function into the objective function, and perform minimum processing on the objective function to obtain the optimal solution of the objective function. Based on the optimal solution, the power generation strategy data is formulated, and the power generation strategy data includes the power generation strategy data and power storage strategy data of the second distributed power generation unit and the energy storage submodule in each time period. The power generation strategy model then performs minimum processing on the objective function after the second solution set is substituted, iteratively calculates the objective function, and continuously adjusts the parameter values ​​in the second solution set until the set of parameters that minimize the objective function value is found. In the minimum processing process, optimization algorithms such as gradient descent, stochastic gradient descent, and Newton's method are used to efficiently search for the global or local minimum of the objective function. After the minimum processing, the power generation strategy model can determine the set of parameters that minimize the objective function value, that is, the optimal solution. This optimal solution contains key information such as the power generation and storage of the second distributed power generation unit and the energy storage submodule in each time period. Based on the optimal solution, the power generation strategy model further formulates the power generation strategy data. These data describe in detail the power generation strategy data and power storage strategy data of the second distributed power generation unit and energy storage submodule in each period, including the power generation in each period, the charging and discharging strategy of the energy storage submodule, the allocation ratio of different energy types, etc. It provides clear guidance for oilfield operations and ensures the efficient use of energy and sustainable development of the environment.

[0041] The energy driving module is used to drive the second distributed power generation unit and the energy storage submodule based on the above power generation strategy data.

[0042] Furthermore, the above-mentioned energy drive module is also used to formulate distributed power generation strategy data for each power generation unit in the second distributed power generation unit based on the power generation strategy data, the position of each power generation unit in the second distributed power generation unit, and the position of each operating equipment in the operating equipment system module.

[0043] Specifically, the energy drive module first receives the power generation strategy data generated by the power generation strategy model. These data describe in detail the power generation and storage strategies of the second distributed power generation unit and the energy storage submodule in each time period. The energy drive module obtains the location information of each power generation unit in the second distributed power generation unit and the location information of each operating equipment in the operating equipment system module through the geographic information system (GIS) or the site layout map. The energy drive module analyzes these location information to determine the relative distance and layout relationship between the power generation unit and the operating equipment. Based on the power generation strategy data and location analysis, the energy drive module formulates distributed power generation strategy data for each power generation unit. These strategy data are intended to optimize the utilization of the power generation unit, reduce transmission losses, and ensure the power supply of the operating equipment. When formulating the strategy, the energy drive module considers the capacity and efficiency of the power generation unit, the power demand of the operating equipment, the transmission distance and loss between the power generation unit and the operating equipment, the energy cost and the environmental impact. By comprehensively considering these factors, the energy drive module assigns appropriate power generation tasks to each power generation unit and determines its power generation and power generation mode in each time period. According to the formulated distributed generation strategy data, the energy drive module drives each power generation unit in the second distributed generation unit to control the start, shutdown, and power regulation of the power generation unit. During the implementation process, the energy drive module will continuously collect the operating data of the power generation unit and the operating equipment, and conduct real-time analysis and optimization. If it is found that the power generation efficiency of a power generation unit decreases or the transmission loss increases, the energy drive module will adjust its power generation strategy in time to ensure the stable operation and efficient utilization of the entire system. At the same time, the energy drive module also monitors the charging and discharging status of the energy storage submodule to ensure that it can provide power support when needed.

[0044] The operation driving module is used to drive the operation equipment in the above operation equipment system module in sequence based on the operation plan data of the target oil field.

[0045] Specifically, the operation driver module receives the target oilfield operation plan data from the oilfield management system, including key information such as the type, quantity, operation sequence, and operation time of the operation equipment. The received operation plan data is parsed to extract specific operation instructions and operation sequence. The operation driver module assigns corresponding tasks to each operation equipment according to the instructions and sequence. According to the parsed operation plan and equipment status check results, the operation driver module will drive each operation equipment in the operation equipment system module in order, start the equipment, adjust the equipment parameters, and monitor the operation of the equipment.

[0046] The anti-empty pumping module is used to determine the working status of each oil well in each target oil field based on the operating equipment data. If an oil well in an empty pumping state is detected, an empty pumping stop instruction is generated and sent to the designated equipment to obtain the target oil field operation plan update data, and the above operation plan update data is sent to the energy decision-making module.

[0047] Specifically, the anti-empty pumping module receives key parameters such as the pumping rate, liquid level, motor current, etc. of the oil well from the operating equipment system module. After data cleaning, data format conversion, data verification and other steps, the accuracy and integrity of the data are ensured. The anti-empty pumping module uses the pre-processed data to analyze the working status of each oil well. By comparing the parameters such as the pumping rate and liquid level of the oil well with the preset threshold, it is determined whether the oil well is in an empty pumping state. The empty pumping state refers to the oil well being that during the oil pumping process, due to reasons such as too low liquid level or oil pump failure, the liquid pumped out is mainly gas or air, and there is almost no oil. Once it is detected that the oil well is in an empty pumping state, the anti-empty pumping module will conduct further confirmation. This may include repeated detection, data analysis, troubleshooting and other operations to ensure the accuracy of the detection. After confirming that the oil well is in an empty pumping state, the anti-empty pumping module will generate an empty pumping stop instruction. The instruction contains specific operations to stop pumping, such as shutting down the oil pump and adjusting the pumping rate. The anti-empty pumping module sends the generated empty pumping stop instruction to the designated operating equipment, such as the oil pump control system, oil well monitoring system, etc. These devices will perform corresponding operations according to the instructions to stop the empty pumping state. After stopping the empty pumping state, the anti-empty pumping module will interact with the oilfield management system to obtain the updated operation plan data of the target oilfield. These data may include adjusted oil pumping plans, oil well maintenance plans, etc. The anti-empty pumping module sends the obtained operation plan update data to the energy decision module. The energy decision module will adjust the overall energy allocation and operation plan based on these data to ensure the efficiency and safety of oilfield operations.

[0048] The present invention provides a microgrid load flexible intelligent management and control system, the system comprising: a distributed energy module, including a power generation submodule and an energy storage submodule, the power generation submodule is used to supply energy to the operating equipment system module of the target oil field, the energy storage submodule is used to power the microgrid measurement and control module, wherein the power generation submodule comprises a first distributed power generation unit and a second distributed power generation unit, the power generation mode of the first distributed power generation unit is renewable energy power generation, and the second distributed power generation unit is non-renewable energy power generation; the operating equipment system module comprises a machine mining unit, a heat unit, a water injection unit and an auxiliary unit, which are used to perform oil and gas mining operations on the target oil field; the microgrid measurement and control module is used to collect energy from the first distributed power generation unit Equipment data, environmental data of the target oil field and operating equipment data of each operating equipment in the operating equipment system module; an energy decision module, which is used to input energy equipment data, operating equipment data, environmental data, historical energy equipment data, historical operating equipment data, historical environmental data and target oil field operating plan data into the power generation strategy model to obtain power generation strategy data, and the above historical energy equipment data, historical operating equipment data and target oil field operating plan data are stored in a database; an energy drive module, which is used to drive the second distributed power generation unit and the energy storage submodule based on the above power generation strategy data; an operating drive module, which is used to drive the operating equipment in the above operating equipment system module in sequence based on the operating plan data of the target oil field.

[0049] The implementation methods of each step in a microgrid load flexible intelligent management and control system provided by an embodiment of the present invention have been described in detail in any of the above system embodiments, so they will not be repeated here.

[0050] In this embodiment, a microgrid load flexible intelligent management and control system is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

Claims

1. A microgrid load flexible intelligent management and control system, characterized in that: The system comprises: A distributed energy module, comprising a power generation submodule and an energy storage submodule, wherein the power generation submodule is used to supply energy to the operating equipment system module of the target oil field, and the energy storage submodule is used to supply power to the microgrid measurement and control module, wherein the power generation submodule comprises a first distributed power generation unit and a second distributed power generation unit, wherein the power generation mode of the first distributed power generation unit is renewable energy power generation, and the second distributed power generation unit is non-renewable energy power generation; The operating equipment system module includes a mechanical extraction unit, a thermal unit, a water injection unit and an auxiliary unit, which are used to perform oil and gas extraction operations on the target oil field; A microgrid measurement and control module, used to collect energy equipment data of the first distributed power generation unit, environmental data of the target oil field, and operating equipment data of each operating equipment in the operating equipment system module; An energy decision module, used for inputting energy equipment data, operating equipment data, environmental data, historical energy equipment data, historical operating equipment data, historical environmental data and target oilfield operating plan data into a power generation strategy model to obtain power generation strategy data, wherein the historical energy equipment data, historical operating equipment data and target oilfield operating plan data are stored in a database; An energy driving module, used to drive the second distributed power generation unit and the energy storage submodule based on the power generation strategy data; An operation driving module, used for driving the operation equipment in the operation equipment system module in order based on the operation plan data of the target oil field; The power generation strategy model includes an objective function and a neural network model, wherein the objective function is: f(C gen_2 ,E stored ,E wasted ,L demand )=αC gen_2 +βE stored +γE wasted +δ(L demand -L supplied ) 2 Among them, C gen_2 Characterizes the power generation of the second distributed generation unit, E stored Represents the electrical energy stored in the energy storage submodule, E wasted Represents the electrical energy wasted due to excess power generation, L demand Characterizes the total load demand of the operating equipment system module, L supplied Represents the actual electric energy supplied to the operating equipment system module, α, β, γ, and δ represent the non-renewable energy weight coefficient, storage weight coefficient, waste weight coefficient, and equipment supply-demand difference weight coefficient respectively; In the power generation strategy model, the first constraint is: P total (t)=P gen_1 (t)+P gen_2 (t)>L demand (t) E stored (t+1)=E stored (t)+P excess (t)Δt If P gen_1 (t)>L demand (t) and the energy storage submodule reaches the upper limit of energy storage, then E wasted (t) = P excess (t)Δt Among them, P total (t) represents the output of the electronic module at time t, P gen_1 (t) represents the output of the first distributed generation unit at time t, P gen_2 (t) represents the output of the second distributed generation unit at time t, Δt represents the time step, L demand (t) represents the demand of the operating equipment system module at time t, P excess (t) represents the additional output of the electronic module at time t.

2. The system according to claim 1, characterized in that When the current stored power of the energy storage submodule is less than a preset threshold, the second distributed power generation unit generates power for the energy storage submodule.

3. The system according to claim 2, characterized in that In the power generation strategy model, the second constraint is: AND stored_min ≤E stored ≤E stored_max P charge_max ≥P excess (t)≥-P discharge_max Among them, P charge_max and P discharge_max Respectively represent the maximum charging power and maximum discharging power of the energy storage submodule.

4. The system according to claim 3, characterized in that In the power generation strategy model, the third constraint is: L demand (t)=L mining (t)+L heat (t)+L water (t)+L aux (t) Among them, L mining (t), L heat (t), L water (t) and L aux (t) represent the load requirements of the mining unit, heat unit, water injection unit and auxiliary unit at time t, respectively. Characterizes the time series data corresponding to the job plan data, ΔL tol Characterizes the load demand deviation range, It represents the maximum energy supply at time t.

5. The system according to claim 4, characterized in that The neural network model is used to determine a second solution set of the objective function based on a first solution set of the objective function, historical energy equipment data, historical operating equipment data, historical environmental data and target oilfield operating plan data, wherein the first solution set is obtained based on the objective function, a first constraint condition, a second constraint condition and a third constraint condition.

6. The system according to claim 5, characterized in that The power generation strategy model is also used to substitute the second solution set of the objective function into the objective function, and perform minimum processing on the objective function to obtain the optimal solution of the objective function. Based on the optimal solution, power generation strategy data is formulated, and the power generation strategy data includes the power generation strategy data and power storage strategy data of the second distributed power generation unit and the energy storage sub-module in each time period.

7. The system according to claim 1, characterized in that The microgrid load flexible intelligent management and control system also includes: The anti-empty pumping module is used to determine the working status of each oil well in each target oil field based on the operating equipment data. If an oil well in an empty pumping state is detected, an empty pumping stop instruction is generated and sent to the designated equipment to obtain the target oil field operation plan update data, and the operation plan update data is sent to the energy decision module.

8. The system according to claim 1, characterized in that The energy drive module is also used to formulate distributed power generation strategy data for each power generation unit in the second distributed power generation unit based on the power generation strategy data, the position of each power generation unit in the second distributed power generation unit, and the position of each operating equipment in the operating equipment system module.

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